Abstract: A high resistance gallium oxide quality prediction method based on deep learning and an edge-defined film-fed crystal growth method, a preparation method and a system; the quality prediction method includes the following steps: obtaining preparation data of a high resistance gallium oxide single crystal prepared by the edge-defined film-fed crystal growth method, the preparation data including seed crystal data, environment data and control data, and the control data including doping element concentration and doping element type; preprocessing the preparation data to obtain preprocessed preparation data; inputting the preprocessing preparation data into a trained neural network model, acquiring the predicted quality data corresponding to the high resistance gallium oxide single crystal through the trained neural network model, the predicted quality data including predicted resistivity.

Abstract: A quality prediction method, a preparation method and a system of conductive gallium oxide based on deep learning and Czochralski method. The quality prediction method includes the steps of obtaining preparation data of conductive gallium oxide single crystal prepared by Czochralski method. The preparation data includes a seed crystal data, an environmental data, and a control data. The environmental data includes doping element concentration and doping element type; preprocessing the preparation data to obtain a preprocessed preparation data; preparing the preprocessed data is input to a trained neural network model, and a predicted quality data corresponding to the conductive gallium oxide single crystal is obtained through the trained neural network model, and the predicted quality data includes a predicted carrier concentration.

Abstract: A quality prediction method, a preparation method and a system of high resistance gallium oxide based on deep learning and Czochralski method. The quality prediction method includes the steps of obtaining preparation data of high resistance gallium oxide single crystal prepared by Czochralski method. The preparation data includes a seed crystal data, an environmental data, and a control data. The environmental data includes doping element concentration and doping element type; preprocessing the preparation data to obtain a preprocessed preparation data; preparing the preprocessed data is input to a trained neural network model, and a predicted quality data corresponding to the high resistance gallium oxide single crystal is obtained through the trained neural network model, and the predicted quality data includes a predicted resistivity.

Abstract: A Group VB element doped with a ?-gallium oxide crystalline material, and a preparation method and application thereof. The series doped with the ?—Ga2O3 crystalline material is monoclinic, the space group is C2/m, the resistivity is in the range of 2.0×10?4 to 1×104?·cm, and/or the carrier concentration is in the range of 5×1012 to 7×1020/cm3. The preparation method comprises steps of: mixing M2O5 and Ga2O3 with a purity of 4N or more at molar ratio of (0.000000001-0.01):(0.999999999-0.99); an then performing crystal growth. The present invention can prepare a high-conductivity ?-Ga2O3 crystalline material with n-type conductivity characteristics by conventional processes, providing a basis for applications thereof to electrically powered electronic devices, optoelectronic devices, photocatalysts or conductive substrates.

Abstract: A Group VB element doped with a ?-gallium oxide crystalline material, and a preparation method and application thereof. The series doped with the ?—Ga2O3 crystalline material is monoclinic, the space group is C2/m, the resistivity is in the range of 2.0×10?4 to 1×104?·cm, and/or the carrier concentration is in the range of 5×1012 to 7×1020/cm3. The preparation method comprises steps of: mixing M2O5 and Ga2O3 with a purity of 4N or more at molar ratio of (0.000000001-0.01):(0.999999999-0.99); an then performing crystal growth. The present invention can prepare a high-conductivity ?-Ga2O3 crystalline material with n-type conductivity characteristics by conventional processes, providing a basis for applications thereof to electrically powered electronic devices, optoelectronic devices, photocatalysts or conductive substrates.